COVID-19 Data and Maps:

Data used from https://www1.nyc.gov/site/doh/covid/covid-19-data.page

See link: https://github.com/nychealth/coronavirus-data

from IPython.display import HTML

HTML('''<script>
code_show=true; 
function code_toggle() {
 if (code_show){
 $('div.input').hide();
 } else {
 $('div.input').show();
 }
 code_show = !code_show
} 
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_toggle()"><input type="submit" value="Click here to toggle on/off the raw code."></form>''')

import pandas as pd

from matplotlib import pyplot as plt

import numpy as np

%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import folium

df = pd.read_csv("https://raw.githubusercontent.com/nychealth/coronavirus-data/master/tests-by-zcta.csv")
df = df.dropna()

pop = pd.read_csv("/Users/apapiu/Downloads/2010+Census+Population+By+Zipcode+(ZCTA) (1).csv")

pop = pop.rename(columns={"Zip Code ZCTA":"Zip Code", "2010 Census Population":"Population"})

df = df.rename(columns = {"MODZCTA":"Zip Code"})

df["Zip Code"] = df["Zip Code"].astype("int")

df = pd.merge(pop[["Zip Code", "Population"]], df, on = "Zip Code")

df["Perc"] = (df["Positive"]/df["Population"])*100

df["Zip Code"] = df["Zip Code"].astype("str")

m = folium.Map(location=[40.693943, -73.985880], default_zoom_start=11, tiles='cartodbpositron')

folium.Choropleth(
   geo_data="https://raw.githubusercontent.com/gdobler/nycep/master/d3/data/nyc-zip-code.json", # I found this NYC zipcode boundaries by googling 
             data=df[["Zip Code", "Positive"]], # my dataset
             columns=['Zip Code', 'Positive'], # zip code is here for matching the geojson zipcode, sales price is the column that changes the color of zipcode areas
             key_on='feature.properties.ZIP', # this path contains zipcodes in str type, this zipcodes should match with our ZIP CODE column
             fill_color='Purples',
             bins=[0, 0.1, 100, 250, 500, 750, 1000, 1250, 1500, 1700],
             fill_opacity=0.75, 
             line_opacity=0.3, 
             name= "Number of Confirmed Positive",
             nan_fill_color='grey'
).add_to(m)

# folium.GeoJson(
#     data = 'vic_for_crime_2018.geojson',
#     style_function = style_function    
# ).add_to(world_map)

<folium.features.Choropleth at 0x121d9bd90>

Number of Cases by Zip Code:

m

m = folium.Map(location=[40.693943, -73.985880], default_zoom_start=12, tiles='cartodbpositron')

folium.Choropleth(
   geo_data="https://raw.githubusercontent.com/gdobler/nycep/master/d3/data/nyc-zip-code.json", # I found this NYC zipcode boundaries by googling 
             data=df[["Zip Code", "Perc"]], # my dataset
             columns=['Zip Code', 'Perc'], # zip code is here for matching the geojson zipcode, sales price is the column that changes the color of zipcode areas
             key_on='feature.properties.ZIP', # this path contains zipcodes in str type, this zipcodes should match with our ZIP CODE column
             fill_color='Purples',
             bins=[0, 0.2, 0.5, 0.75, 1, 1.25, 1.5,  2.3],
             fill_opacity=0.75, line_opacity=0.3, name = "Percentage Positive",
             nan_fill_color='grey' 
).add_to(m)

<folium.features.Choropleth at 0x1242a7290>

Percentage Infected by Zip Code:

m

x